Method and apparatus for assigning a confidence level to a term within a user knowledge profile

ABSTRACT

A method of assigning a confidence level to a term within an electronic document, such as an e-mail, includes the step of firstly determining a quantitative indicator in the exemplary form of an occurrence value, based on the number of occurrences of a particular term within an electronic document, and associating the occurrence term within the relevant term. Thereafter, a qualitative indicator, based on a quality of the term, is determined. For example, the qualitative indicator may be determined utilizing the parts of speech of words comprising the term. A confidence level value, which may be utilized to indicate a relative importance of the term in describing a user knowledge base, is then generated utilizing the quantitative and qualitative indicators.

FIELD OF THE INVENTION

[0001] The present invention relates generally to the field of knowledgemanagement and, more specifically, to a method and apparatus forassigning a confidence level to a term within a user knowledge profile.

BACKGROUND OF THE INVENTION

[0002] The new field of “knowledge management” (KM) is receivingincreasing recognition as the gains to be realized from the systematiceffort to store and export vast knowledge resources held by employees ofan organization are being recognized. The sharing of knowledge broadlywithin an organization offers numerous potential benefits to anorganization through the awareness and reuse of existing knowledge, andthe avoidance of duplicate efforts.

[0003] In order to maximize the exploitation of knowledge resourceswithin an organization, a knowledge management system may be presentedwith two primary challenges, namely (1) the identification of knowledgeresources within the organization and (2) the distribution and accessingof information regarding such knowledge resources within theorganization.

[0004] The identification, capture, organization and storage ofknowledge resources is a particularly taxing problem. Prior artknowledge management systems have typically implemented knowledgerepositories that require users manually to input information frequentlyinto pre-defined fields, and in this way manually and in a promptedmanner to reveal their personal knowledge base. However, this approachsuffers from a number of drawbacks in that the manual entering of suchinformation is time consuming and often incomplete, and therefore placesa burden on users who then experience the inconvenience and cost of acorporate knowledge management initiative long before any direct benefitis experienced. Furthermore, users may not be motivated to describetheir own knowledge and to contribute documents on an ongoing basis thatwould subsequently be re-used by others without their awareness orconsent. The manual input of such information places a burden on userswho then experience the inconvenience and cost of a corporate knowledgemanagement initiative long before any direct benefit is experienced.

[0005] It has been the experience of many corporations that knowledgemanagement systems, after some initial success, may fail because eithercompliance (i.e., the thoroughness and continuity with which each usercontributes knowledge) or participation (i.e., the percentage of usersactively contributing to the knowledge management system) falls toinadequate levels. Without high compliance and participation, it becomesa practical impossibility to maintain a sufficiently current andcomplete inventory of the knowledge of all users. Under thesecircumstances, the knowledge management effort may never offer anattractive relationship of benefits to costs for the organization as awhole, reach a critical mass, and the original benefit of knowledgemanagement falls apart or is marginalized to a small group.

[0006] In order to address the problems associated with the manual inputof knowledge information, more sophisticated prior art knowledgemanagement initiatives may presume the existence of a centralized staffto work with users to capture knowledge bases. This may however increasethe ongoing cost of knowledge management and requires a larger up-frontinvestment before any visible payoff, thus deterring the initial fundingof many an otherwise promising knowledge management initiatives. Even ifan initial decision is made to proceed with such a sophisticatedknowledge management initiative, the cash expenses associated with alarge centralized knowledge capture staff may be liable to come underattack, given the difficulty of quantifying knowledge managementbenefits in dollar terms.

[0007] As alluded to above, even once a satisfactory knowledgemanagement information base has been established, the practicalutilization thereof to achieve maximum potential benefit may bechallenging. Specifically, ensuring that the captured information isreadily organized, available, and accessible as appropriate throughoutthe organization may be problematic.

SUMMARY OF THE INVENTION

[0008] According to a first aspect of the invention, there is provided amethod of assigning a confidence level to a term within an electronicdocument. A first quantitative indicator, based on a number ofoccurrences of the term within the electronic document, is determined. Acharacteristic indicator, based on a characteristic of the term, isdetermined. A second indicator is assigned to the term, the secondquantitative indicator being derived from the first quantitativeindicator and the characteristic indicator.

[0009] According to a second aspect of the invention, there is providedapparatus for assigning a confidence level to a term within anelectronic document. A term extractor extracts of the term from theelectronic document. Confidence logic determines a first indicator basedon a number of occurrences of the term within the electronic document.The confidence logic also determines a characteristic indicator based ona characteristic of the term, and assigns a second quantitativeindicator, derived from the first quantitative indicator and thecharacteristic indicator, to the term.

[0010] Other features of the present invention will be apparent from theaccompanying drawings and from the detailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

[0012]FIG. 1 is a block diagram illustrating a knowledge managementsystem, according to an exemplary embodiment of the present invention.

[0013]FIG. 2 is a block diagram illustrating a knowledge site managementserver, according to an exemplary embodiment of the present invention.

[0014]FIG. 3 is a block diagram illustrating a knowledge access server,according to an exemplary embodiment of the present invention.

[0015]FIG. 4 is a block diagram illustrating a knowledge converter,according to an exemplary embodiment of the present invention.

[0016]FIG. 5 is a block diagram illustrating a client software program,and an e-mail message generated thereby, according to an exemplaryembodiment of the present invention.

[0017]FIG. 6 is a block diagram illustrating the structure of aknowledge repository, according to an exemplary embodiment of thepresent invention, as constructed from the data contained in arepository database and a user database.

[0018]FIG. 7 is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of constructing a userknowledge profile.

[0019]FIG. 8 is a flowchart illustrating a high-level method, accordingto an exemplary embodiment of the present invention, by which terms maybe extracted from an electronic document and by which confidence levelvalues may be assigned to such terms.

[0020]FIG. 9A is a flowchart illustrating a method, according toexemplary embodiment of the present invention, of determining aconfidence level for a term extracted from an electronic document.

[0021]FIG. 9B is a flowchart illustrating a method, according toexemplary embodiment of the present invention, by which a documentweight value may be assigned to a document based on addresseeinformation associated with the document.

[0022]FIG. 10 illustrates a term-document binding table, according to anexemplary embodiment of the present invention.

[0023]FIG. 11 illustrates a weight table, according to an exemplaryembodiment of the present invention.

[0024]FIG. 12 illustrates an occurrence factor table, according to anexemplary embodiment of the present invention.

[0025]FIG. 13 illustrates a confidence level table, including initialconfidence level values, according to an exemplary embodiment of thepresent invention.

[0026]FIG. 14 illustrates a modified confidence level table, includingmodified confidence level values, according to an exemplary embodimentof the present invention.

[0027]FIG. 15A is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of constructing a userknowledge profile that includes first and second portions.

[0028]FIG. 15B is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of storing a term ineither a first or a second portion of a user knowledge profile.

[0029]FIG. 16A illustrates a user-term table, constructed according tothe exemplary method illustrated in FIG. 15A.

[0030]FIG. 16B illustrates a user-term table, constructed according tothe exemplary method illustrated in FIG. 15A.

[0031]FIG. 17A is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of facilitating access toa user knowledge profile.

[0032]FIG. 17B is a flowchart illustrating an alternative method,according to exemplary embodiment of the present invention, offacilitating access to a user knowledge profile.

[0033]FIG. 17C is a flowchart illustrating a method, according toexemplary embodiment of the present invention, of performing a publicprofile process.

[0034]FIG. 17D is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of performing a privateprofile process.

[0035]FIG. 17E is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of performing a profilemodification process.

[0036]FIG. 18A is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of addressing anelectronic document for transmission over a computer network.

[0037]FIG. 18B is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of executing an “explain”function that provides the reasons for the proposal of an e-mailrecipient.

[0038]FIG. 18C is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of executing a “more”function that proposes further potential recipients for an e-mailmessage.

[0039]FIG. 18D illustrates a user dialog, according to an exemplaryembodiment of the present invention, through which a list of potentialrecipients is displayed to an addressor of an e-mail message.

[0040]FIG. 19 is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of managing userauthorization to publish, or permit access to, a user knowledge profile.

[0041]FIG. 20 is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of assigning a confidencevalue, either in the form of a confidence level value or a confidencememory value, to a term.

[0042]FIG. 21 is a flowchart illustrating a method, according to anexemplary embodiment of the present invention, of determining oridentifying a confidence value, either in the form of a confidence levelvalue or a confidence memory value, for a term.

[0043]FIG. 22 illustrates a user-term table, according to an exemplaryembodiment of the present invention, that is shown to include aconfidence level value column, a confidence memory value column and atime stamp column.

[0044]FIG. 23 is a block diagram illustrating a machine, according toone exemplary embodiment, within which software in the form of a seriesof machine-readable instructions, for performing any one of the methodsdiscussed above, may be executed.

DETAILED DESCRIPTION

[0045] A method and apparatus for assigning a confidence level to a termwithin a user knowledge profile are described. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of the presentinvention. It will be evident, however, to one skilled in the art thatthe present invention may be practiced without these specific details.

Overview

[0046] With a view to addressing the above described difficultiesassociated with manual knowledge capture either by a profile owner or bya dedicated staff, there is provided a method and apparatus forcapturing knowledge automatically, without excessive invasion ordisruption of normal work patterns of participating users. Further, thepresent specification teaches a method and apparatus whereby a databaseof captured knowledge information is maintained continuously andautomatically, without requiring that captured knowledge informationnecessarily be visible or accessible to others. The presentspecification also teaches facilitating the user input and modificationof a knowledge profile associated with the user in a knowledge databaseat the user's discretion.

[0047] The present specification teaches a method and apparatus forintercepting electronic documents, such as for example e-mail messages,originated by a user, and extracting terms therefrom that arepotentially indicative of a knowledge base of the originating user. Theextracted knowledge terms may then be utilized to construct a userknowledge profile. The grammatical structure, length, frequency anddensity with which the extracted knowledge terms occur within electronicdocuments originated by a user, and prior history of use of theextracted knowledge terms within an organization may furthermore beutilized to attach a metric, in the form of a confidence level value, tothe relevant knowledge terms for the purpose of grouping, ranking, andprioritizing such knowledge terms. Knowledge terms may furthermore bestored in either a private or public portion of the user knowledgeprofile, depending upon the confidence level values thereof.

[0048] It will be appreciated that the large volume of e-mail messagestraversing an e-mail system over a period of time will contain a largenumber of terms that may be irrelevant to the identification of theknowledge base of a user. With a view to determining which terms aretruly indicative of a knowledge base, a number of rules (or algorithms)may be exercised with respect to extracted terms to identify terms thatare candidates for inclusion within a public portion of the userknowledge profile. Further rules (or algorithms) may be applied to anassembled knowledge profile for the purpose of continually organizingand refining the profile.

[0049] Corporate e-mail systems have become increasingly pervasive, andhave become an accepted medium for idea communication withincorporations. Accordingly, the content of e-mail messages flowing withina large organization amounts to a vast information resources that, overthe course of time, may directly or indirectly identify knowledge basesheld by individuals within the organization.

[0050] The present specification also teaches addressing privacyconcerns associated with the examination of e-mail messages for theabove purposes by providing users with the option selectively to submitoriginated e-mail messages for examination, or alternatively to bypassthe examination and extraction system of the present invention.

[0051] There is also taught a computer-implemented method and apparatusfor addressing an electronic document, such as an e-mail message, fortransmission over a computer network. The e-mail message may be examinedto identify terms therein. The identified terms are then compared to anumber of user knowledge profiles with a view to detecting apredetermined degree of correspondence between the identified terms andany one or more of the user knowledge profiles. In the event that apredetermined degree of correspondence is detected, the sender of theelectronic document is prompted to the either accept or decline theproposed recipient as an actual recipient of the electronic document,after first being offered an opportunity to inspect the specific basisof the correspondence between the identified terms and the proposedrecipients. The e-mail message may also be parsed to extract recipientsentered manually by the user. The degree of correspondence between theknowledge profiles of the manually entered recipients and the identifiedterms of the message is then optionally used as the basis ofrecommendations to the user that certain manually entered recipients bedropped from the ultimate list of recipients.

[0052] This aspect of the present teachings is advantageous in that asender of an e-mail message is presented with a list of proposedrecipients, identified according to their knowledge profiles and thecontent of the e-mail message, who may be interested in receiving thee-mail message. Accordingly, the problems of over-distribution andunder-distribution of e-mail messages that may be encountered within anorganization may be reduced. Specifically, in the over-distributionsituation, many users are frequently copied on e-mail messages,resulting in lost productivity as the users struggle to cope withincreasing volumes of daily e-mail. Further, when the time available toread e-mail messages becomes restricted, users typically begin to deferreading of e-mail messages, and communication efficiency within theorganization may be adversely affected. In the under-distributionsituation, it may occur that the proper recipients of the message arenot included in the distribution list, and accordingly fall “out of theloop”.

[0053] There is also taught a method of facilitating a user profilequery or look-up wherein, in response to a match between a query and auser profile, the owner of the user profile may be prompted forauthorization to publish all (or a portion) of the user profile to theoriginator of the query or to others generally. This is advantageous inthat it addresses the above mentioned privacy concerns by treating theknowledge profile as a confidential resource under the control of theuser. The user is thus also able to control the timing, circumstancesand extent to which it is made accessible to others. A further advantageis that the user is prompted for input specifically to satisfy specific,pending requests of others. This relieves the user of the need toremember to modify his or her profile on a regular basis and the need tomake decisions concerning the composition of the profile prospectively,prior to any actual use of the profile by others. In this manner theuser saves time and effort, since the determination that manualinteraction with the profile is necessary is a function of the presentsystem, not a responsibility of the user.

[0054] There is also taught a method of assigning a confidence levelvalue to a term within an electronic document. This confidence levelvalue is based on a first quantitative indicator, derived from thenumber of occurrences of the term within the electronic document, and asecond characteristic indicator, derived utilizing the characteristic ofthe term.

[0055] For the purposes of the present application, the word “term”shall be taken to include any acronym, word, collection of words,phrase, sentence, or paragraph. The term “confidence level” shall betaken to mean any indication, numeric or otherwise, of a level within apredetermined range.

System Architecture

[0056]FIG. 1 is a block diagram illustrating a knowledge managementsystem 10, according to an exemplary embodiment of the presentinvention. The system 10 may conveniently be viewed as comprising aclient system 12 and a server system 14. The client system 12 maycomprise one or more clients, such as browser clients 16 and e-mailclients 18, that are resident on terminals or computers coupled to acomputer network. In one exemplary embodiment, each of the browserclients 16 may comprise the Internet Explorer client developed byMicrosoft Corp. of Redmond, Wash., or the Netscape Navigator clientdeveloped by Netscape Communications of Menlo Park, Calif. Each of thee-mail clients 18 may further comprise the Outlook Express, Outlook 97,Outlook 98 or Netscape Communicator e-mail programs. As will bedescribed in further detail below, the browser and e-mail clients 16 arecomplemented by extensions 19, that enable the e-mail clients 18 to sendan electronic message (e.g., either an e-mail or HTML document) to aknowledge server 22 implemented on the server side 14 of the system 10.As shown in FIG. 1, the extensions 19 may be integral with an e-mailclient 18, or external to the client 18 and in communication therewith.The clients 16 and 18 may default to sending every communication to arelevant component of the knowledge server 22, while allowing a userspecifically to designate a communication not suitable for transmissionto the knowledge server 22. The user designation may be facilitatedthrough controls that are installed as software modules which interactwith or modify an e-mail client 18, and which cause messages to becopied to a special e-mail address (e.g., a Knowledge Server (KS)mailbox 25 maintained by a e-mail server 23) associated with a knowledgeserver component. In the case where a client extension 19 for performingthis automatic transmission is not available, the user can manually addthe e-mail address of the KS mailbox 25 to the list of recipients forthe message. Further details in this regard are provided below. Filesembedded within an e-mail message, such as attachments, may also beselectively included or excluded from the capture process and may alsobe selectively included or excluded from retention in a knowledgerepository.

[0057] The browser clients 16 are used as an additional means to submitdocuments to the knowledge server 22 at the discretion of a user. Thebrowser client 16 is used to access an interface application 34,maintained on a web server 20, which transmits documents to theknowledge server 22.

[0058] In alternate embodiments, a client may also propagate a list ofbookmarks, folders or directories to the knowledge server 22 for thepurpose of user knowledge profile construction.

Server Side Architecture

[0059] The server side 14 of the system 10 includes the web server 20,the e-mail server 23 and the knowledge server 22. The web server 20 maybe any commercially available web server program such as InternetInformation Server (IIS) from Microsoft Corporation, the NetscapeEnterprise Server, or the Apache Server for UNIX. The web server 20includes the interface application 34 for interfacing with the knowledgeserver 22. The web server 20 may run on a single machine that also hoststhe knowledge server 22, or may alternatively run along with theinterface application 34 on a dedicated web server computer. The webserver 20 may also be a group of web server programs running on a groupof computers to thus enhance the scalability of the system 10. As theweb server 20 facilitates access to a local view of a knowledgerepository 50, maintained by the knowledge access server 26, by thebrowser clients 16, the web server interface application 34 implementsknowledge application interfaces, knowledge management interfaces, userprofile creation and maintenance interfaces, and a server managementinterface. The web server 20 also facilitates knowledge profile queries,e-mail addressing to an e-mail client 18, and any other access to theknowledge server 22 using the standard HTTP (web) protocol.

[0060] The knowledge server 22 includes a knowledge site managementserver (KSMS) 27 and the knowledge access server (KAS) 26. The knowledgeserver access 26 includes an interface that provides a local view of aknowledge repository 50, which is physically stored in the user database56A and a repository database 56B. The knowledge site management server27 is shown to have access to the local view of the knowledge repository50 maintained by the knowledge access server 26. The illustratedcomponents of the knowledge server 22 are collectively responsible forthe capture (termed “knowledge discovery”) of terms indicative of a userknowledge base and for the distribution of user knowledge profileinformation. Knowledge discovery may be done by the examination andprocessing of electronic documents, such as e-mail messages, which maybe propagated to the e-mail server 23 from an e-mail client 18 via theSimple Mail Transfer Protocol (SMTP), as shown at 32. Alternatively,knowledge discovery may be implemented by the examination of submissionsfrom a browser client 16 via the web server 20.

[0061] The knowledge server 22 includes the knowledge access server 26and the knowledge site management server 27 as two separate and distinctserver systems in view of the divergent functions provided by theservers 26 and 27. Specifically, the knowledge site management server 27functions primarily to manage non-interactive processing (e.g., theextraction of knowledge from inbound e-mail messages), to manage theuser information database 56A, and to implement various centralizedsystem management processes. The knowledge site management server 27does not communicate interactively with clients 18, or with clients 16except for administrative functions. The knowledge access server 26, onthe other hand, functions primarily to respond to queries and updatesfrom users submitted via clients, typically browser clients 16. Multipleinstances of a knowledge access server 26 may be required to support alarge corporate environment and to provide appropriate scalability;however only one knowledge site management server 27, one user database56A, and one repository database 56B typically exist in a workingsystem. In small scale environments, the web server 20, knowledge accessserver 26, and knowledge site management server 27, and even the e-mailserver 23, may all optionally be deployed on the same physical computer.

[0062]FIG. 2 is a block diagram illustrating an exemplary embodiment,according to the present invention, of the knowledge site managementserver 27. The server 27 is shown to include a socket front-end 40 tofacilitate communication with the web server 20 for administrativerequests, a request handler 44, a knowledge gathering system 28, aknowledge converter 24, and a variety of specialized controller modules45A-45C. The request handler 44, upon receiving a request from the webserver 20 via the interface application 34 and socket front-end 40,starts a session to process the request such as, for example, a requestby an authorized systems administrator to configure the behavior of theknowledge gathering system 28.

[0063] The knowledge gathering system 28 is shown in FIG. 2 to includean extraction controller 47, a mail system interface 42, and a termextractor 46 including confidence logic 45. The extraction controller 47commands the mail system interface 42 to retrieve messages submitted bythe e-mail client extensions 19 to the KS mailbox 25 on the e-mailserver 23 for the purpose of extraction and processing. The extractioncontroller 47 can request this continuously or periodically on ascheduled basis, so that messages can be processed at a convenient timewhen computing resources are lightly loaded, for example, overnight. Themail system interface 42 retrieves e-mail messages from the e-mailserver 23 using the Simple Mail Transfer Protocol (SMTP), Post OfficeProtocol 3 (POP3), or Internet Message Access Protocol 4 (IMAP4)protocols. The mail system interface 42 propagates electronic documentsdirectly to a term extractor 46, including confidence logic 45, thatoperates to convert electronic documents into per-user knowledgeprofiles that are stored in a knowledge repository 50. The termextractor 46 may include any commercially available term extractionengine (such as “NPTOOL” from LingSoft Inc. of Helsinki, Finland, or“Themes” from Software Scientific) that analyzes the electronicdocument, recognizes noun phrases in the document, and converts suchphrases to a canonical form for subsequent use by the confidence logic45 as candidate terms in a knowledge profile.

[0064] The term extractor 46 performs a variety of the steps whenparsing and decoding an electronic document, such as interpreting anyspecial attributes or settings encoded into the header of the message ofthe e-mail client 18, resolving the e-mail addresses of recipientsagainst either the built-in user database or an external user database,preprocessing the electronic document, extracting noun-phrases from thetext as candidates for knowledge terms, processing these knowledgeterms, and storing summary information about the document and extractionprocess in the databases 56A and 56B. The term extractor 46 furtherdetects and strips out non-original texts, attachments and in some casesthe entire electronic document based on the document not meetingpredetermined minimum criteria. Further details regarding the exactprocedures implemented by the term extractor 46 will be provided below.Once the term extractor 46 has extracted the knowledge terms, theknowledge repository 50 is updated. Specifically, new terms are added,and repetitions of known terms are used to update the knowledgerepository 50.

[0065] The knowledge repository 50 is defined by a hierarchicalstructure of classes. The objects of these classes represent theknowledge information that includes, inter alia, user profiles(including knowledge profiles) and organizational structure, and arestored in two databases: the user database 56A and the repositorydatabase 56B. The repository database 56B contains profile andrepository information and can use one of a number of commercialrelational database management systems that support the Open DataBaseConnectivity (ODBC) interface standard. A database interface 54 providesa logical database-independent class API to access the physicaldatabases and to shield the complete server codes from accessingdatabase native API so that the server process can use any relationaldatabase management system (RDMS). Because the repository database 56Ais open to inspection by systems administrators, and may be hosted on anexisting corporate system, special measures may be taken to enhance theprivacy of information in the repository database 56B; for example, therepository database 56B contains no actual user names or e-mailaddresses, but instead may use encrypted codes to represent users in amanner that is meaningful only in combination with the user database.The user database 56A is a small commercial RDBMS embedded into theknowledge repository 50 in such a way that it cannot be accessed exceptthrough the interfaces offered by the system 10. The user database 56Acontains encrypted identifying codes that allow the names of actualusers to be associated with e-mail addresses, login IDs, passwords, andprofile and repository information in the repository database.

[0066] A lexicon controller 45C is responsible for building tables ofassociated terms. Terms are considered “associated” with each other tothe extent that they tend to co-occur in close proximity within thedocuments of multiple users. The lexicon controller 45C manages thebackground process of data mining that is used to discover associationsbetween terms and record those in special association tables within therepository database 56B.

[0067] A profile controller 45B is a module that may optionally beincluded within the knowledge site management server 27, and manages aqueue of pending, compute-intensive operations associated with updatingprofiles. Since the algorithm for the confidence level value calculationof a term (embodied in the confidence logic 45) depends on the totalnumber of documents profiled, the confidence level value for each andevery term in a user's profile is technically obsolete when any documentis profiled. The profile controller 45B manages the “recalculation” ofprofiles. The actual operation is performed within the knowledge accessserver 26, which has a knowledge repository 50 interface.

[0068] A case controller 45A keeps track of open cases and initiatesnotifications to users concerning their status. A “case” is a pendingrequest from one user to another, as will be detailed below. Forexample, if a user requests an expert in a certain field via a clientbrowser client 16, the knowledge access server 26 matches the termagainst both the public and private portions of all user profiles. If ahigh confidence, but private, match is found, the system cannot revealthe identity of the matched person to the inquirer and must thereforeopen a “case”. The case places a notification in the profile “home” pageof the target user and/or transmits an e-mail message with a link backto that page. The target user may then (via a browser):

[0069] 1. See the identity of the inquirer and the basis of the match.

[0070] 2. See comments added by the inquirer.

[0071] 3. Deny the request, at which point the case is closed.

[0072] 4. Put a block on any further matches from that person or basedon that term.

[0073] 5. Go into the profile and edit the term responsible for thematch.

[0074] 6. Indicate that the case is accepted and provide authorizationto reveal the identity of the target to the inquirer.

[0075] From the perspective of the inquirer, private matches areinitially returned with a match strength only and do not reveal the nameof the person or document matched. The user can then initiate cases forany or all of these private matches, based on how urgently theinformation is needed, how good the matches were, and whether the publicmatches are sufficient. Each case gets an expiration date set by theinquirer and notification options regarding how the inquirer wants to betold about the disposition of the case. Open cases are summarized in theWeb area for the inquirer, along with the date and query that generatedthe return values. If the target denies a case, that status iscommunicated to the user. The user has no option to send e-mail orotherwise further identify that person. If the target accepts the case,the identity of the target is communicated to the user by updating thecase record and the case is closed. Case history retention options are asite administration option.

[0076]FIG. 3 is a block diagram illustrating the components thatconstitute the knowledge access server 26. The knowledge access server26 is shown to include a socket front-end 40 to facilitate communicationwith the web server interface application 34. The knowledge accessserver 26 further includes a request handler 44, a term extractor 46, aknowledge repository 50 and a database interface 54 that function in amanner similar to that described above with reference to the knowledgegathering system 28. The term extractor 46 includes comparison logic 51,the functioning of which will be described below. The knowledge accessserver 26 functions primarily as an interface between knowledge usersand the knowledge repository 50. It provides services to the web serverinterface application 34, which implements a number of user interfacesas described above for interacting with the knowledge repository 50.

[0077]FIG. 4 is a block diagram illustrating the components thatconstitute the knowledge converter 24. The knowledge converter 24 isshown to include a term extractor 46 that is fed from an array of formatconverters 60. The knowledge converter 24 is able to access theknowledge repository 50, and to import data from other knowledgesystems, or export knowledge to other knowledge systems, via each of theformat converters 60.

[0078] Returning to FIG. 1, the knowledge access server 26 implementsthe interface to the knowledge repository 50 and the knowledge sitemanagement server 27 is shown to access the knowledge repository 50 viathe knowledge access server 26. FIGS. 3 and 4 illustrate data for theknowledge repository 50 as residing in databases 56A and 56B. Thedatabases 56A and 56B are built on a general database interface 54 andprovide persistent storage for the core system classes referred toabove. In one exemplary embodiment of the present invention, the userdatabase and the repository databases are implemented utilizing theMicrosoft SQL server, developed by Microsoft Corp. of Redmond Wash., toprovide default storage management services for the system. However,programming may be done at a more general level to allow forsubstitution of other production class relational database managementsystems, such as those developed by Sybase, Oracle or Informix.

Client Side Architecture

[0079]FIG. 5 is a diagrammatic representation of a client, according toan exemplary embodiment of the present invention, in the form of ane-mail client 18. It will be appreciated that the e-mail client 18 maybe any commercially available e-mail client, such as a MicrosoftExchange, Outlook Express, Outlook 97/98 or Lotus Notes client. Thee-mail client 18 includes modifications or additions, in the form of theextensions 19, to the standard e-mail client to provide additionalfunctionality. Specifically, according to an exemplary embodiment of thepresent invention, three subsystems are included within the e-mailclient extensions 19, namely a user interface 80, a profiling system 82,and an addressing system 84.

[0080] The profiling system 82 implements properties on an originatedmessage, as well as menu and property sheet extensions at global andmessage levels for users to set and manipulate these new properties.More specifically, profiling system 82 provides a user with a number ofadditional options that determine how a message 85 propagated from thee-mail client 18 to the knowledge repository 50 will be processed andhandled for the purposes of knowledge management. A number of theprovided options are global, while others apply on a per-message basis.For example, according to one exemplary embodiment, the followingper-message options (or flags) may be set by a user to define theproperties of an e-mail message:

[0081] 1. An “Ignore” flag 86 indicating the e-mail message should notbe processed for these purposes of constructing or maintaining a userknowledge profile, and should not be stored.

[0082] 2. A “Repository” parameter 88 indicating that the message may beprocessed for the purposes of constructing a knowledge profile and thenstored in the repository 50 for subsequent access as a document byothers. The “Repository” parameter 88 also indicates whether thedocument (as opposed to terms therein) is to be stored in a private orpublic portion of the repository 50.

[0083] A number of global message options may also be made available toa user for selection. For example, an e-mail address (i.e., the KSmailbox 25 or the e-mail server 23) for the knowledge server 22 may beenabled, so that the e-mail message is propagated to the server 22.

[0084] Actual implementation and presentation of the above per-messageand global options to the user may be done by the addition of acompanion application or set of software modules which interact withAPI's provided by e-mail clients, or modules which modify the e-mailclient itself, which are available during message composition. If theuser activates the Ignore flag 86, the profiling system 82 will not makeany modifications to the message and no copy of the message will be sentto the knowledge gathering system 28 via the KS mailbox 25. Otherwise,per-message options, once obtained from the user, are encoded.Subsequently, when the user chooses to send the message 85 using theappropriate control on the particular e-mail client 18, the e-mailaddress of the knowledge gathering server is appended to the blind copylist for the message. The profiling system 82 encrypts and encodes thefollowing information into the message header, for transmission to anddecoding by the knowledge gathering system 28, in accordance withInternet specification RFC 1522:

[0085] 1. The list of e-mail addresses in the “to:” and “cc:” lists;

[0086] 2. Per-message options as appropriate; and

[0087] 3. For those recipients suggested by the addressing system 84(see below), a short list of topic identifiers including the primarytopics found within the message and the primary topics found within theuser profile that formed a basis of a match.

[0088] 4. Security information to validate the message as authentic.

[0089] When the message 85 is sent over the normal e-mail transport, thefollowing events occur:

[0090] 1. Recipients on the “to:” and “cc:” lists will receive a normalmessage with an extra header containing the encoded and encryptedoptions. This header is normally not displayed by systems that reade-mail and can be ignored by recipients;

[0091] 2. The recipients will not be aware that the knowledge gatheringsystem has received a blind copy of the message; and

[0092] 3. If the sender chooses to archive a copy of the message 85, thee-mail address of the knowledge gathering system 28 will be retained inthe “bcc” field as a reminder that the message was sent to the knowledgegathering server.

[0093] Further details concerning the addressing system 86 will bediscussed below.

The Repository

[0094]FIG. 6 is a block diagram illustrating the structure of therepository 50, according to one exemplary embodiment of the presentinvention, as constructed from data contained in the repository database56B, and the user database 56A. The repository 50 is shown to include anumber of tables, as constructed by a relational database managementsystem (RDBMS). Specifically, the repository 50 includes a user table90, a term table 100, a document table 106, a user-term table 112, aterm-document table 120 and a user-document table 130. The user table 90stores information regarding users for whom knowledge profiles may beconstructed, and includes an identifier column 92, including unique keysfor each entry or record within the table 90. A name column 94 includesrespective names for users for whom knowledge profiles are maintainedwithin the repository 50. A department column 96 contains a descriptionof departments within an organization to which each of the users may beassigned, and an e-mail column 98 stores respective e-mail addresses forthe users. It will be appreciated that the illustrated columns aremerely exemplary, and a number of other columns, storing furtherinformation regarding users, may be included within the user table 90.

[0095] The term table 100 maintains a respective record for each termthat is identified by the term extractor 46 within an electronicdocument, and that is included within the repository 50. The term table100 is shown to include an identifier column 102, that stores a uniquekey for each term record, and a term column 104 within which the actualextracted and identified terms are stored. Again, a number of furthercolumns may optionally be included within the term table 100. Thedocument table 106 maintains a respective record for each document thatis processed by the term extractor 46 for the purposes of extractingterms therefrom. The document table 106 is shown to include anidentifier column 108, that stores a unique key for each documentrecord, and a document name column 110, that stores an appropriate namefor each document analyzed by the term extractor 46.

[0096] The user-term table 112 links terms to users, and includes atleast two columns, namely a user identifier column 114, storing keysidentifying users, and a term identifier column 116, storing keysidentifying terms. The user-term table 112 provides a many-to-manymapping of users to terms. For example, multiple users may be associatedwith a single term, and a single user may similarly be associated withmultiple terms. The table 112 further includes a confidence level column118, which stores respective confidence level values, calculated in themanner described below, for each user-term pair. The confidence levelvalue for each user-term pair provides an indication of how strongly therelevant term is coupled to the user, and how pertinent the term is indescribing, for example, the knowledge base of the relevant user.

[0097] The term-document table 120 links terms to documents, andprovides a record of which terms occurred within which document.Specifically, the term-document table 120 includes a term identifiercolumn 122, storing keys for terms, and a document identifier column124, storing keys for documents. The table 120 further includes anadjusted count column 126, which stores values indicative of the numberof occurrences of a term within a document, adjusted in the mannerdescribed below. For example, the first record within the table 120records that the term “network” occurred within the document “e-mail 1”2.8 times, according to the adjusted count.

[0098] The user-document table 130 links documents to users, andincludes at least two columns, namely a user identifier column 132,storing keys identifying users, and a document identifier column 134,storing keys identifying various documents. For example, the firstrecord within the exemplary user-document table 130 indicates that theuser “Joe” is associated with the document “e-mail 1”. This associationmay be based upon the user being the author or recipient of the relevantdocument.

Identification of Knowledge Terms and the Calculation of AssociatedConfidence Level Values

[0099]FIG. 7 is a flow chart illustrating a method 140, according to anexemplary embodiment of the present invention, of constructing a userknowledge profile. FIG. 7 illustrates broad steps that are described infurther detail with reference to subsequent flow charts and drawings.The method 140 commences at step 142, and proceeds to decision box 144,wherein a determination is made as to whether an electronic document,for example in the form of an e-mail propagated from an e-mail client18, is indicated as being a private document. This determination may bemade at the e-mail client 18 itself, at the e-mail server 23, or evenwithin the knowledge site management server 27. This determination mayfurthermore be made by ascertaining whether the Ignore flag 86,incorporated within an e-mail message 85, is set to indicate the e-mailmessage 85 as private. As discussed above, the Ignore flag 86 may be setat a users discretion utilizing the profiling system 82, accessed viathe user interface 80 within the extensions 19 to the e-mail client 18.In the event that the electronic document is determined to be private,the method 140 terminates at step 146, and no further processing of theelectronic document occurs. Alternatively, the method 140 proceeds tostep 148, where confidence level values are assigned to various termswithin the electronic document. At step 150, a user knowledge profile isconstructed utilizing the terms within the electronic document to whichconfidence level values were assigned at step 148. The method 140 thenterminates at step 146.

[0100]FIG. 8 is a flow chart illustrating a high-level method 148,according to an exemplary embodiment of the present invention, by whichterms may be extracted from an electronic document, and by whichconfidence level values may be assigned such terms. The method 148comprises two primary operations, namely a term extraction operationindicated at 152, and a confidence level value assigning operation,indicated at a 154. The method 148 implements one methodology by whichthe step 148 shown in FIG. 7 may be accomplished. The method 148 beginsat step 160, and then proceeds to step 162, where an electronicdocument, such as for example an e-mail, a database query, a HTMLdocument and or a database query, is received at the knowledge sitemanagement server 27 via the mail system interface 42. For the purposesof explanation, the present example will assume that an e-mail message,addressed to the KS mailbox 25, is received at the knowledge sitemanagement server 27 via the mail system interface 42, from the e-mailserver 23. At step 164, terms and associated information are extractedfrom the electronic document. Specifically, the e-mail message ispropagated from the mail system interface 42 to the term extractor 46,which then extracts terms in the form of, for example, grammar terms,noun phrases, word collections or single words from the e-mail message.The term extractor 46 may further parse a header portion of the e-mailto extract information therefrom that is required for the maintenance ofboth the repository and user databases 56B and 56A. For example, theterm extractor 46 will identify the date of transmission of the e-mail,and all addressees. The term extractor 46 will additionally determinefurther information regarding the electronic document and terms therein.For example, the term extractor 46 will determine the total number ofwords comprising the electronic document, the density of recurring wordswithin the document, the length of each term (i.e., the number of wordsthat constitute the term), the part of speech that each word within thedocument constitutes, and a word type (e.g., whether the word is alexicon term). To this end, the term extractor 46 is shown in FIG. 2 tohave access to a database 49 of lexicon terms, which may identify bothuniversal lexicon terms and environment lexicon terms specific to anenvironment within which the knowledge site management server 27 isbeing employed. For example, within a manufacturing environment, thecollection of environment lexicon terms will clearly differ from thelexicon terms within an accounting environment.

[0101] Following the actual term extraction, a first relevancy indicatorin the form of an adjusted count value is calculated for each termwithin the context of the electronic document at step 168. At step 170,a second relevancy indicator in the form of a confidence level iscalculated for each term within the context of multiple electronicdocuments associated with a particular user. Further details regardingsteps 168 and 170 are provided below. The method 148 then terminates atstep 172.

[0102]FIG. 9A is a flow chart illustrating a method 154, according to anexemplary embodiment of the present invention, of determining aconfidence level for a term extracted from an electronic document.Following the commencement step 180, a term and associated informationis received at the confidence logic 45, included within the termextractor 46. While the confidence logic 45 is shown to be embodied inthe term extractor 46 in FIG. 2, it will be appreciated that theconfidence logic 45 may exist independently and separately of the termextractor 46. In one embodiment, the associated information includes thefollowing parameters:

[0103] 1. A count value indicating the number of occurrences of the termwithin a single electronic document under consideration;

[0104] 2. A density value, expressed as a percentage, indicating thenumber of occurrences of the term relative to the total number of termswithin the electronic document;

[0105] 3. A length of value indicating the total number of wordsincluded within the relevant term;

[0106] 4. A Part of Speech indication indicating the parts of speechthat words included within the term comprise (e.g., nouns, verbs,adjectives, or adverbs); and

[0107] 5. A Type indication indicating whether the term comprises auniversal lexicon term, an environment lexicon term, or is of unknowngrammatical structure.

[0108] At step 184, a “binding strength”, indicative of how closely theterm is coupled to the electronic document under consideration, isdetermined. While this determination may be made in any number of ways,FIG. 10 shows an exemplary term-document binding table 200, utilizingwhich a class may be assigned to each of the extracted terms.Specifically, the term-document binding table 200 is shown to includethree columns, namely a “number of occurrences” column 202, a densitycolumn 204, and an assigned class column 206. A term having a densityvalue of greater than four percent, for example, is identified asfalling in the “A” class, a term having a density of between two andfour percent is identified as falling in the “B” class, a term having adensity of between one and two percent is identified as falling in the“C” class, while a term having a density of between 0.5 and one percentis identified as falling in the “D class. For the terms having a densityof above 0.5 percent, the density value is utilized to assign a class.For terms which have a density value less than 0.5 percent, the countvalue is utilized for this purpose. Specifically, a term having a countvalue of greater than 3 is assigned to the “E” class, and a term havinga count value of between 1 and 3 is assigned to the “F” class.Accordingly, the assigned class is indicative of the “binding strength”with which the term is associated with or coupled to the electronicdocument under consideration.

[0109] At step 186, a characteristic (or qualitative) indicator in theform of a term weight value is determined, based on characteristicsqualities of the term such as those represented by the Type and Part ofSpeech indications discussed above. While this determination may againbe made in any number of ways, FIG. 11 shows an exemplary weight table210, utilizing which a weight value may be assigned to each of theextracted terms. Specifically, the weight table 210 is shown to includefour columns, namely a weight column 212, a type column 214, a lengthcolumn 216 and a Part of Speech column 218. By identifying anappropriate combination of type, length and Part of Speech indications,an appropriate term weight value is assigned to each term. In the typecolumn 214, a type “P” indication identifies an environment lexiconterm, a type “L” indication identifies a universal lexicon term, and atype “U” indication identifies a term of unknown grammatical structurefor a given length. The entries within the length column 216 indicatethe number of words included within the term. The entries within thePart of Speech column 218 indicate the parts of speech that the wordswithin a term comprise. The “A” indication identifies the adjectives,the “V” indication identifies a verb, the “N” indication identifies anoun, and the “X” indication identifies an unknown part of speech. Bymapping a specific term to an appropriate entry within the weight table210, an appropriate term weight value, as indicated in the weight column212, may be assigned to the term.

[0110] At step 188, a relevancy quantitative indicator in the form of anadjusted count value for each term, is calculated, this adjusted countvalue being derived from the binding strength and term weight valuescalculated at steps 184 and 186. While this determination may again bemade in any number of ways, FIG. 12 shows an exemplary occurrence factortable 220, utilizing which an adjusted count value for the relevant termmay be determined. The occurrence factor table 220 is shown to includevalues for various binding strength/term weight value combinations. Theadjusted count value is indicative of the importance or relevance ofterm within a single, given document, and does not consider theimportance or relevance of the term in view of any occurrences of theterm in other electronic documents that may be associated with aparticular user.

[0111] At step 190, a determination is made as to whether any adjustedcount values exists for the relevant term as a result of the occurrenceof the term in previously received and analyzed documents. If so, theadjusted count values for occurrences of the term in all such previousdocuments are summed.

[0112] At step 192, an initial confidence level values for the term isthen determined based on the summed adjusted counts and the term weight,as determined above with reference to the weight table 210 shown in FIG.11. To this end, FIG. 13 illustrates a confidence level table 230, whichincludes various initial confidence level values for various summedadjusted count/weight value combinations that may have been determinedfor a term. For example, a term having a summed adjusted count of 0.125,and a weight value of 300, may be allocated an initial confidence levelvalue of 11.5. Following the determination of an initial confidencelevel value, confidence level values for various terms may be groupedinto “classes”, which still retain cardinal meaning, but whichstandardize the confidence levels into a finite number of “confidencebands”. FIG. 14 illustrates a modified table 240, derived from theconfidence level table 230, wherein the initial confidence levelsassigned are either rounded up or rounded down to certain values. Bygrouping into classes by rounding, applications (like e-mailaddressing), can make use of the classes without specificknowledge/dependence on the numerical values. These can then be tunedwithout impact to the applications. The modified confidence level valuesincluded within the table 240 may have significance in a number ofapplications. For example, users may request that terms with aconfidence level of greater than 1000 automatically be published in a“public” portion of their user knowledge profile. Further, e-mailaddressees for a particular e-mail may be suggested based on a matchbetween a term in the e-mail and a term within the user knowledgeprofile having a confidence level value of greater than, merely forexample, 600.

[0113] The method 154 then terminates at step 194.

[0114] In a further embodiment of the present invention, the method 154,illustrated in FIG. 9A, may be supplemented by a number of additionalsteps 195, as illustrated in FIG. 9B, by which a “document weight” valueis assigned to a document based on addressee information associated withthe document. The document weight value may be utilized in any one ofthe steps 182-192 illustrated in FIG. 9A, for example, as a multiplyingfactor to calculate a confidence level value for a term. In oneexemplary embodiment, the binding strength value, as determined at step184, may be multiplied by the document weight value. In anotherexemplary embodiment, the term weight value, as determined at step 186,may be multiplied by the document weight value.

[0115] The document weight value may be calculated by the confidencelogic 45 within the term extractor 46. Referring to FIG. 9B, at step196, the confidence logic 45 identifies the actual addresseeinformation. To this end, the term extractor 46 may include a headerparser (not shown) that extracts and identifies the relevant addresseeinformation. At step 197, the confidence logic 45 then accesses adirectory structure that may be maintained by an external communicationprogram for the purposes of determining the level of seniority within anorganization of the addressees associated with the document. In oneexemplary embodiment of the invention, the directory structure may be aLightweight Directory Access Protocol (LDAP) directory maintained by agroupware server, such as Microsoft Exchange or Lotus Notes. At step198, a cumulative seniority level for the various addressees isdetermined by summing seniority values for each of the addressees. Atstep 199, the summed seniority value is scaled to generate the documentweight value. In this embodiment, the cumulative or summed senioritylevel of the various addressees comprises an “average” seniority valuethat is used for the purpose of calculating the document weight term.Alternatively, instead of summing in the seniority values at step 198, a“peak” seniority value (i.e., a seniority value based on the senioritylevel of the most senior addressee) may be identified and scaled at step199 to generate the document weight value.

[0116] In alternative embodiments, the addressee information may beutilized in a different manner to generate a document weight value.Specifically, a document weight value may be calculated based on thenumber of addressees, with a higher number of addressees resulting in agreater document weight value. Similarly, a document weight value may becalculated based on the number of addressees who are included within aspecific organizational boundary (e.g., a specific department ordivision). For example, an e-mail message addressed primarily to anexecutive group may be assigned a greater document weight value than ane-mail message addressed primarily to a group of subordinates. Further,the document weight value may also be calculated using any combinationof the above discussed addressee information characteristics. Forexample, the document weight value could be calculated using bothaddressee seniority and addressee number information.

Construction of a User Knowledge Profile

[0117]FIG. 15A is a flow chart illustrating a method 250, according toone exemplary embodiment of the present invention, of constructing auser profile that includes first and second portions that mayconveniently be identified as “private” and “public” portions.Specifically, unrestricted access to the “public” portion of the userknowledge profile may be provided to other users, while restrictedaccess to the “private” portion may be facilitated. For example,unrestricted access may encompass allowing a user to review detailsconcerning a user knowledge profile, and the target user, responsive toa specific request and without specific authorization from the targetuser. Restricted access, on the other hand, may require specificauthorization by the target user for the provision of informationconcerning the user knowledge profile, and the target user, in responseto a specific request. The method 250 commences at step 252, and thenproceeds to step 254, where a determination is made regarding theconfidence level value assigned to a term, for example using the method154 described above with reference to FIG. 9A. Having determined theconfidence level value, the method 250 proceeds to step 256, where athreshold value is determined. The threshold value may either be adefault value, or a user specified value, and is utilized to categorizethe relevant term. For example, users may set the threshold through thebrowser interface as a fundamental configuration for their profile. Ifset low, the user profile will be aggressively published to the publicside. If set high, only terms with a high level of confidence will bepublished. Users can also elect to bypass the threshold publishingconcept altogether, manually reviewing each term that crosses thethreshold (via the notification manager) and then deciding whether topublish. At decision box 258, a determination is made as to whether theconfidence level value for the term is less than the threshold value. Ifso, this may be indicative of a degree of uncertainty regarding the termas being an accurate descriptor of a user's knowledge. Accordingly, atstep 260, the relevant term is then stored in the “private” portion ofthe user knowledge profile. Alternatively, should the confidence levelvalue be greater than the threshold value, this may be indicative of agreater degree of certainty regarding the term as an accurate descriptorof a user's knowledge, and the relevant term is then stored in the“public” portion of the user's knowledge profile at step 262. The method150 then terminates at step 264.

[0118]FIG. 16A shows an exemplary user-term table 112, constructedaccording to the method 250 illustrated in FIG. 15A. Specifically, thetable 112 is shown to include a first user knowledge profile 270 and asecond user knowledge profile 280. The first user knowledge profile 270is shown to include a “public” portion 272, and a “private” portion 274,the terms within the “private” portion 274 having an assigned confidencelevel value (as indicated in the confidence level column 118) below athreshold value of 300. The second user knowledge profile 280 similarlyhas a “public” portion 282 and a “private” portion 284.

[0119] The exemplary user-term table 112 shown in FIG. 16A comprises anembodiment of the table 112 in which the public and private portions aredetermined dynamically with reference to a confidence level valueassigned to a particular user-term pairing. FIG. 16B illustrates analternative embodiment of the user-term table 112 that includes a“private flag” column 119, within which a user-term pairing may beidentified as being either public or private, and accordingly part ofeither the public or private portion of a specific user profile. Whilethe state of a private flag associated with a particular user-termpairing may be determined exclusively by the confidence level associatedwith the pairing, in an alternative embodiment of the invention, thestate of this flag may be set by other mechanisms. For example, asdescribed in further detail below with reference to FIG. 17E, a user maybe provided with the opportunity manually to modify the private orpublic designation of a term (i.e., move a term between the public andprivate portions of a user knowledge profile). A user may be providedwith an opportunity to modify the private or public designation of aterm in response to a number of events. Merely for example, a user maybe prompted to designate a term as public in response to a “hit” upon aterm in the private portion during a query process, such as during an“expertlookup” query or during an “addressee-lookup” query. When storingthe term in the user knowledge profile at either steps 260 or 262, theallocation of the term to the appropriate portion may be made by settinga flag, associated with the term, in the “private flag” column 119within the user-term table 112, as illustrated in FIG. 16B. For example,a logical “1” entry within the “private flag” column 119 may identifythe associated term as being in the “private” portion of the relevantuser knowledge profile, while a logical “0” entry within the “privateflag” column 119 may identify the associated term as being in the“public” portion of the relevant user knowledge profile.

[0120]FIG. 15B illustrates an exemplary method 260/262, according to oneembodiment of the present invention, of storing a term in either apublic or private portion of a user knowledge profile. Specifically, arespective term is added to a notification list at step 1264, followingthe determination made at decision box 258, as illustrated in FIG. 15A.At decision box 1268, a determination is made as to whether apredetermined number of terms have been accumulated within thenotification list, or whether a predetermined time period has passed. Ifthese conditions are not met, the method waits for additional terms tobe added to the notification list, or for further time to pass, at step1266, before looping back to the step 1264. On the other hand, should acondition within the decision box 1268 have been met, the methodproceeds to step 1270, where the notification list, that includes apredetermined number of terms that are to be added to the user knowledgeprofile, is displayed to a user. The notification list may be providedto the user in the form of an e-mail message, or alternatively the usermay be directed to a web site (e.g., by a URL included within e-mailmessage) that displays the notification list. In yet a furtherembodiment, the notification list may be displayed on a web or intranetpage that is frequently accessed by the user, such as a home page. Atstep 1272, the user then selects terms that are to be included in thepublic portion of the user knowledge profile. For example, the user mayselect appropriate buttons displayed alongside the various terms withinthe notification list to identify terms for either the public or privateportions of the user knowledge profile. At step 1274, private flags,such as those contained within the “private flag” column 119 of theuser-term table 112 as shown in FIG. 16B, may be set to a logical zero“0” to indicate that the terms selected by the user are included withinthe public portion. Similarly, private flags may be set to a logical one“1” to indicate terms that were not selected by the user for inclusionwithin the public portion are by default included within the privateportion. It will of course be appreciated that the user may, at step1272, select terms to be included within the private portion, in whichcase un-selected terms will by default be included within the publicportion. The method then ends at step 1280.

[0121] The above described method is advantageous in that a user is notrequired to remember routinely to update his or her user profile, but isinstead periodically notified of terms that are candidates for inclusionwithin his or her user knowledge profile. Upon notification, the usermay then select terms for inclusion within the respective public andprivate portions of the user knowledge profile. As such, the method maybe viewed as a “push” model for profile maintenance.

Methods of Accessing a User Knowledge Profile

[0122] While the above method 250 is described as being executed at thetime of construction of a user knowledge profile, it will readily beappreciated that the method may be dynamically implemented as requiredand in response to a specific query, with a view to determining whetherat least a portion of a user knowledge profile should be published, orremain private responsive to the relevant query. To this end, FIG. 17Ashows a flow chart illustrating a method 300, according to one exemplaryembodiment of the present invention, of facilitating access to a userknowledge profile. The method 300 commences at step 302, and thenproceeds to step 304, where a threshold value is determined. At step306, a document term within an electronic document generated by a user(hereinafter referred to as a “query” user) is identified. Step 306 isperformed by the term extractor 46 responsive, for example, to thereceipt of an e-mail from the mail system interface 42 within theknowledge gathering system 28. At step 308, comparison logic 51 withinthe term extractor 46 identifies a knowledge term within the repository50 corresponding to the document term identified at step 306. Thecomparison logic 51 also determines a confidence level value for theidentified knowledge term. At decision box 310, the comparison logic 51makes a determination as to whether the confidence level value for theknowledge term identified at step 308 is less than the threshold valueidentified at step 304. If not (that is the confidence level value isgreater than the threshold value) then a public profile process isexecuted at step 312. Alternatively, a private profile process isexecuted at step 314 if the confidence level value falls below thethreshold value. The method 300 then terminates at step 316.

[0123]FIG. 17B shows a flowchart illustrating an alternative method 301,according to an exemplary embodiment of the present invention, offacilitating access to a user knowledge profile. The method 301commences at step 302, and then proceeds to step 306, where a documentterm within an electronic document generated by a user (i.e., the“query” user) is identified. The term extractor 46 performs step 306responsive, for example, to the receipt of an e-mail message from themail system interface 42 within the knowledge gathering system 28. Atstep 308, the comparison logic 51 within the term extractor 46identifies a knowledge term within the knowledge repository 50corresponding to the document term identified at step 306. At decisionbox 311, the comparison logic 51 then makes a determination as towhether a “private” flag for the knowledge term is set to indicate therelevant knowledge term as being either in the public or the privateportion of a user knowledge profile. Specifically, the comparison logic51 may examine the content of an entry in the private flag column 112 ofa user-term table for a specific user-term pairing of which theknowledge term is a component. If the “private” flag for the knowledgeterm is set, thus indicating the knowledge term as being in the privateportion of a user knowledge profile, the private profile process isexecuted at step 314. Alternatively, the public profile process isexecuted at step 312. The method 301 then terminates at step 316.

[0124]FIG. 17C shows a flow chart detailing a method 312, according toan exemplary embodiment of the present invention, of performing thepublic profile process mentioned in FIGS. 17A and 17B. The method 312commences at step 320, and user information, the knowledge termcorresponding to the document term, and the confidence level valueassigned to the relevant knowledge term are retrieved at steps 322, 324,and 326. This information is then displayed to the query user at step328, whereafter the method 312 terminates at step 330.

[0125]FIG. 17D shows a flow chart detailing a method 314, according toan exemplary embodiment of the present invention, of performing theprivate profile process mentioned in FIGS. 17A and 17B. The method 314commences at step 340, and proceeds to step 342, where a user (hereinafter referred to as the “target” user) who is the owner of theknowledge profile against which the hit occurred is notified of thequery hit. This notification may occur in any one of a number of ways,such as for example via an e-mail message. Such an e-mail message mayfurther include a URL pointing to a network location at which furtherinformation regarding the query hit, as well as a number of target useroptions, may be presented. At step 346, the reasons for the query hitare displayed to the target user. Such reasons may include, for example,matching, or similar, document and knowledge terms utilizing which thehit was identified and the confidence level value associated with theknowledge term. These reasons may furthermore be presented within thee-mail propagated at step 342, or at the network location identified bythe URL embedded within the e-mail. At step 348, the target user thenexercises a number-of target user options. For example, the target usermay elect to reject the hit, accept the hit, and/or modify his or heruser knowledge profile in light of the hit. Specifically, the targetuser may wish to “move” certain terms between the public and privateportions of the user knowledge profile. Further, the user may optionallydelete certain terms from the user knowledge profile in order to avoidany further occurrences of hits on such terms. These target user optionsmay furthermore be exercised via a HTML document at the network locationidentified by the URL. At decision box 350, a determination is made asto whether the user elected to modify the user knowledge profile. If so,a profile modification process, which is described below with referenceto FIG. 17E, is executed at step 352. Otherwise, a determination is madeat decision box 354 as to whether the target user rejected the hit. Ifso, the hit is de-registered at step 356. Alternatively, if the targetuser accepted the hit, the public profile process described above withreference to FIG. 17C is executed at step 358. The method 314 thenterminates at step 360.

[0126]FIG. 17E is a flowchart illustrating a method 352, according to anexemplary embodiment of the present invention, for implementing theprofile modification process illustrated at step 352 in FIG. 17D. Themethod 352 commences at step 362, and then proceeds to display step 364,where the target user is prompted to (1) move a term, on which a “hit”has occurred, between the private and public portions of his or her userknowledge profile, or to (2) delete the relevant term from his or heruser knowledge profile. Specifically, the target user may be presentedwith a user dialog, a HTML-enriched e-mail message, or a Web page,listing the various terms upon which hits occurred as a result of aninquiry, besides which appropriate buttons are displayed that allow theuser to designate the term either to the included in the public orprivate portion of his or her user knowledge profile, or that allow theuser to mark the relevant term for deletion from the user knowledgeprofile. At input step 366, the target user makes selections regardingthe terms in the matter described above. At decision box 368, adetermination is made as to whether the user selected terms for transferbetween the public and private portions of the user profile, or forinclusion within the user profile. If so, the method 352 proceeds tostep 370, wherein the appropriate terms are designated as being eitherpublic or private, in accordance with the user selection, by settingappropriate values in the “private flag” column 119 within the user-termtable, as illustrated in FIG. 16B. Thereafter, the method proceeds todecision box 372, wherein a determination is made as to whether the userhas elected to delete any of the terms presented at step 364. If so, therelevant terms are deleted from the user knowledge profile at step 374.The method is then terminates at step 378.

[0127] The methodologies described above with reference to FIGS. 15through 17E are advantageous in that, where the confidence level of aterm falls below a predetermined threshold, the owner of the userknowledge profile may elect to be involved in the process of determiningwhether a query hit is accurate or inaccurate. The owner of the userknowledge profile is also afforded the opportunity to update and modifyhis or her knowledge profile as and when needed. Further, the owner ofthe user knowledge profile is only engaged in the process for hits belowa predetermined certainty level and on a public portion of the knowledgeprofile. Matches between document terms and knowledge terms in thepublic portion are automatically processed, without any manualinvolvement.

Method for Addressing an Electronic Document for Transmission Over aNetwork

[0128] Returning now briefly to FIG. 5, the addressing system 84 withinthe e-mail client extensions 19 operates independently of the profilingsystem 82 to suggest potential recipients for an e-mail message based onthe content thereof. The user interface 80 within the e-mail clientextensions 19 may pop-up a window when the system determines suchsuggestion is possible, based on the length of a draft message beingsent, or may present a command button labeled “Suggest Recipients”. Thisbutton is user selectable to initiate a sequence of operations wherebythe author of the e-mail is presented with a list of potentialrecipients who may be interested in receiving the e-mail based onpredetermined criteria, such as a match between the content of thee-mail and a user profile, or a commonality with a confirmed addressee.

[0129]FIG. 18A is a flow chart illustrating a method 400, according toan exemplary embodiment of the present invention, of addressing anelectronic document, such as an e-mail, for transmission over a network,such as the Internet or an Intranet. The method 400 commences at step402, and then proceeds to step 401, where a determination is made as towhether the body of the draft message exceeds a predetermined length (ornumber of words). If so, content of the electronic document (e.g., ane-mail message body) is transmitted to the knowledge access server 26via the web server 20 at step 404. Specifically, a socket connection isopen between the e-mail client 18 and the web server 20, and the contentof the message body, which may still be in draft form, is transmittedusing the Hypertext Transfer Protocol (HTTP) via the web server 20 tothe knowledge access server 26. At step 406, the knowledge access server26 processes the message body, as will be described in further detailbelow. At step 408, the knowledge access server 26 transmits a potentialor proposed recipient list and associated information to the addressingsystem 84 of the e-mail client 18. Specifically, the informationtransmitted to the e-mail client 18 may include the following:

[0130] 1. A list of user names, as listed within column 94 of the usertable 90, as well as corresponding e-mail addresses, as listed withinthe column 98 of the user table 90;

[0131] 2. A list of term identifiers, as listed in column 116 of theuser-term table 112, that were located within the “public” portion of auser knowledge profile that formed the basis for a match betweendocument terms within the message body and knowledge terms within theuser knowledge profile; and

[0132] 3. A “matching metric” for each user included in the list of usernames (1). Each “matching metric” comprises the sum of the confidencelevel values, each multiplied by the weighted occurrences of the termwithin the message body, for the terms identified by the list of termidentifiers (2) and associated with the relevant user. This “matchingmetric” is indicative of the strength of the recommendation by theknowledge access server 26 that the relevant user (i.e., potentialrecipient) be included within the list of confirmed addressees.

[0133] At step 410, the author of the electronic document is presentedwith a list of potential recipients by the e-mail client 18, andspecifically by the addressing system 84 via a user dialog 440 as shownin FIG. 18D. FIG. 18D groups matching levels into matching classes eachcharacterized by a visual representation (icon).

[0134] The user dialog 440 shown in FIG. 18D presents the list ofpotential recipients in a “potential recipients” scrolling window 442,wherein the names of potential recipients are grouped into levels orranked classes according to the strength of the matching metric. An iconis also associated with each user name, and provides an indication ofthe strength of the recommendation of the relevant potential recipients.Merely for example, a fully shaded circle may indicate a highrecommendation, with various degrees of “blackening” or darkening of acircle indicating lesser degrees of recommendation. A “rejection” iconmay be associated with an actual recipient, and an example of such a“rejection” icon is indicated at 441. The “rejection” icon indicates anegative recommendation on an actual recipient supplied by the author ofthe message, and may be provided in response to a user manuallymodifying his or her profile to designate certain terms therein asgenerating such a “rejection” status for a recipient against which a hitoccurs.

[0135] The user dialog 440 also presents a list of actual (or confirmed)recipients in three windows, namely a “to:” window 442, a “cc:” window444 and a “bcc:” window 446. An inquiring user may move recipientsbetween the potential recipients list and the actual recipients listsutilizing the “Add” and “Remove” buttons indicated at 450. The userdialog 440 also includes an array of “select” buttons 452, utilizingwhich a user can determine the recommendation group to be displayedwithin the scrolling window 442. The user dialog 440 finally alsoincludes “Explained Match” and “More” buttons 454 and 456, the purposesof which is elaborated upon below. As shown in FIG. 18D, the author usermay select an “Explain” function for any of the proposed recipientsutilizing the “Explain Match” button 454. If it is determined atdecision box 412 that this “Explain” function has been selected, themethod 400 branches to step 414, as illustrated in FIG. 18B.Specifically, at step 414, the addressing system 84 propagates a further“Explain” query to the knowledge access server 26 utilizing HTTP, andopens a browser window within which to display the results of the query.At step 416, the knowledge access server 26 retrieves the terms (i.e.,the knowledge terms) that constituted the basis for the match, as wellas associated confidence level values. This information is retrievedfrom the public portion of the relevant user knowledge profile in theknowledge repository 50. At step 418, the information retrieved at step416 is propagated to the client 18 from the knowledge access server 26via the web server 20. The information is then displayed within thebrowser window opened by the e-mail client 18 at step 414. Accordingly,the author user is thus able to ascertain the reason for the proposal ofa potential recipient by the addressing system 84, and to make a moreinformed decision as to whether the proposed recipient should beincluded within the actual recipients (confirmed addressee) list.

[0136] The user also has the option of initiating a “More” function byselecting the “More” button 456 on the user dialog 440, this functionserving to provide the user with additional proposed recipients.Accordingly, a determination is made at step 422 as to whether the“More” function has been selected by the author user. If so, the method400 branches to step 424 as shown in FIG. 18C, where the client 18propagates a “More” request to the knowledge access server 20 in thesame manner as the “Explain” query was propagated to the knowledgeaccess server at step 414. At step 46, the knowledge access server 26identifies further potential recipients, for example, by using athreshold value for the “matching metric” that is lower than a thresholdvalue utilized as a cutoff during the initial information retrievaloperation performed at steps 406 and 408. At step 428, the knowledgeaccess server 26 then transmits the list of further potentialrecipients, and associated information, to the e-mail client 18. At step430, the list of additional potential recipients is presented to theauthor user for selection in descending order according to the “matchingmetric” associated with each of the potential recipients.

[0137] At step 432, the user then adds at his or her option, or deletesselected potential or “rejected” recipients to the list of actualrecipients identified in “to:”, “cc:” or “bcc:” lists of the e-mail,thus altering the status of the potential recipients to actualrecipients. At step 434, the e-mail message is then transmitted to theconfirmed addressees.

[0138] If the user profile includes a “rejection” status on a term(something a user can do through manual modification of the profile),then a special symbol, such as that indicated 441 in FIG. 18D, may bereturned indicating a negative recommendation on a recipient supplied bythe author of the message.

[0139] The exemplary method 400 discussed above is advantageous in thatthe knowledge access server 26 automatically provides the author userwith a list of potential addressees, based on a matching betweendocument terms identified within the message body of an e-mail andknowledge terms included within user profiles.

Case Control

[0140]FIG. 19 is a flow chart illustrating a method 500, according toone exemplary embodiment of the present invention, of managing userauthorization to publish, or permit access to, a user knowledge profile.The method 500 is executed by the case controller 45A that tracks open“cases” and initiates notification to users concerning the status ofsuch cases. For the purposes of the present specification, the term“case” may be taken to refer to a user authorization process forpublication of, or access to, a user knowledge profile. The method 500commences at step 502, and then proceeds to step 504, where a match isdetected with a private portion of a user knowledge profile. At step504, the case controller 45A then opens a case, and notifies the targetuser at step 506 concerning the “hits” or matches between a document (orquery) term and a knowledge term in a knowledge user profile. Thisnotification may be by way of an e-mail message, or by way ofpublication of information on a Web page accessed by the user. At step508, the case controller 45A determines whether an expiration date, bywhich the target user is required to respond to the hit, has beenreached or in fact passed. If the expiration date has passed, the casecontroller 45A closes the case and the method 500 terminates.Alternatively, a determination is made at decision box 510 as to whetherthe target user has responded to the notification by authorizingpublication of, or access to, his or her user knowledge profile based onthe hit on the private portion thereof. If the target user has notauthorized such action (i.e., declined authorization), an inquiring user(e.g., the author user of an e-mail or a user performing a manualdatabase search to locate an expert) is notified of the decline at step512. Alternatively, should the target user have authorized publicationor access, the inquiring user is similarly notified of the authorizationat step 514. The notification of the inquiring user at steps 512 or 514may be performed by transmitting an e-mail to the inquiring user, or byproviding a suitable indication on a web page (e.g., a home page orsearch/query web page) accessed by the inquiring user. At step 516, theappropriate portions of the user profile pertaining to the target userare published to the inquiring user, or the inquiring user is otherwisepermitted access to the user profile. At step 518, the case controller45A then closes the case, whereafter the method terminates.

Supplemental Method of Identifying Confidence Value

[0141] FIGS. 7-9 describe an exemplary method 140 of identifyingknowledge terms and calculating associated confidence level values. Asupplemental method 550, according to an exemplary embodiment of thepresent invention, of assigning a confidence value to a term will now bedescribed with reference to FIGS. 20-22. The supplemental method 550seeks to compensate for a low confidence level value which may beassociated with the term as a result of the term not appearing in anyrecent documents associated with a user. It will be appreciated that bycalculating a confidence level value utilizing the method illustrated inFIG. 9, aged terms (i.e., terms which have not appeared in recentdocuments) may be attributed a low confidence level value even thoughthey may be highly descriptive of a specialization or knowledge of auser. The situation may occur where a user is particularly active withrespect to a particular topic for a short period of time, and thenre-focuses attention on another topic. Over time, the methodologyillustrated in FIG. 9 may too rapidly lower the confidence level valuesassociated with terms indicating user knowledge.

[0142] Referring to FIG. 20, there is illustrated the exemplary method550 of assigning a confidence value to a term. The method 550 commencesat step 552, whereafter an initial confidence memory value (as distinctfrom a confidence level value) is assigned a zero (0) value. At step556, a confidence level value for a term is calculated utilizing, forexample, the method 154 illustrates in FIG. 9. However, this confidencelevel value is only calculated for occurrences of the relevant termwithin a particular time or document window. For example, in summing theadjusted count values at step 190 within the method 154, the adjustedcount values for only documents received within a predetermined time(e.g., the past 30 days), or only for a predetermined number ofdocuments (e.g., the last 30 documents) are utilized to calculate thesummed adjusted count value. It will be appreciated that by discardingdocuments, which occurred before the time or document window, the effecton the confidence level values for aged terms by the absence of suchaged terms within recent documents may be reduced.

[0143] At decision box 558, a determination is then made as to whether anewly calculated confidence level value for a term is greater than apreviously recorded confidence memory value, or alternatively greaterthan a predetermined site-wide or system-wide threshold value. If theconfidence level value is determined to be greater than the confidencememory value (or the threshold value), the confidence memory value isthen made equal to the confidence level value by overwriting theprevious confidence memory value with the newly calculated confidencelevel value. In this way, it is ensured that the confidence level valuedoes not exceed the confidence memory value.

[0144]FIG. 22 is an exemplary user-term table 112, according to oneembodiment of the present invention, that is shown to include aconfidence level column 118, a confidence memory value column 121, and atime stamp column 123. The table 122 records a confidence level valueand a confidence memory value for each user-term pairing within thetable 112, and it is to this table that the confidence level values andthe confidence memory values are written by the method 550. The timestamp column 123 records a date and time stamp value indicative of thedate and time at which the corresponding confidence memory value waslast updated. This value will accordingly be updated upon theoverwriting of the confidence memory value at step 560.

[0145] Should the confidence level value not exceed the confidencememory value or the threshold value, as determined at decision box 558,the method 550 then proceeds to decision box 562, where a furtherdetermination is made as to whether another time or document window,associated with a step of decaying the confidence memory value, hasexpired. If not, the confidence memory value is left unchanged at step564. Alternatively, if the time or document window associated with thedecay step has expired, the confidence memory value is decayed by apredetermined value or percentage at step 566. For example, theconfidence memory value may be decayed by five (5) percent per month.The time stamp value may be utilized to determine the window associatedwith the decay step. The time stamp value associated with the decayedconfidence memory value is also updated at step 566. The method 550 thenterminates at step 568.

[0146]FIG. 21 is a flowchart illustrating an exemplary method 570,according to one embodiment of the present invention, of determining oridentifying a confidence value (e.g., either a confidence level value ora confidence memory value) for a term. The method 570 may be executed inperformance of any of the steps described in the preceding flow chartsthat require the identification of a confidence level value for a termin response to a hit on the term by a document term (e.g., in anelectronic document or other query). The method 570 commences at step572, and proceeds to step 574, where a confidence level value for a termwithin a user profile is identified. For example, the confidence levelvalue may be identified within be user-term table 112 illustrated inFIG. 22. At step 576, a confidence memory value for the term may thenalso be identified, again by referencing the user-term table 112illustrated in FIG. 22. At decision box 578, a determination is thenmade as to whether the confidence level value is greater than theconfidence memory value. If the confidence level value is greater thanthe confidence memory value, the confidence level value is returned, atstep 580, as the confidence value. Alternatively, should the confidencememory value be greater than the confidence level value, the confidencememory value is returned, at step 582, as the confidence value. Themethod 570 then terminates at step 584.

[0147] Accordingly, by controlling the rate at which a confidence valuefor a term is lowered or decayed, the present invention seeks to preventhaving a potentially relevant term ignored or overlooked.

Computer System

[0148]FIG. 23 is a diagrammatic representation of a machine in the formof computer system 600 within which software, in the form of a series ofmachine-readable instructions, for performing any one of the methodsdiscussed above may be executed. The computer system 600 includes aprocessor 602, a main memory 603 and a static memory 604, whichcommunicate via a bus 606. The computer system 600 is further shown toinclude a video display unit 608 (e.g., a liquid crystal display (LCD)or a cathode ray tube (CRT)). The computer system 600 also includes analphanumeric input device 610 (e.g., a keyboard), a cursor controldevice 612 (e.g., a mouse), a disk drive unit 614, a signal generationdevice 616 (e.g., a speaker) and a network interface device 618. Thedisk drive unit 614 accommodates a machine-readable medium 615 on whichsoftware 620 embodying any one of the methods described above is stored.The software 620 is shown to also reside, completely or at leastpartially, within the main memory 603 and/or within the processor 602.The software 620 may furthermore be transmitted or received by thenetwork interface device 618. For the purposes of the presentspecification, the term “machine-readable medium” shall be taken toinclude any medium that is capable of storing or encoding a sequence ofinstructions for execution by a machine, such as the computer system600, and that causes the machine to performing the methods of thepresent invention. The term “machine-readable medium” shall be taken toinclude, but not be limited to, solid-state memories, optical andmagnetic disks, and carrier wave signals.

[0149] Thus, a method and apparatus for assigning a confidence level toa term within a user knowledge profile have been described. Although thepresent invention has been described with reference to specificexemplary embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

What is claimed is:
 1. A method of assigning a confidence level to aterm within an electronic document, the method including the steps of:determining a first quantitative indicator based on a number ofoccurrences of the term within the electronic document; determining acharacteristic indicator based on a characteristic of the term; andassigning a second indicator to the term, the second indicator beingderived from the first quantitative indicator and the characteristicindicator.
 2. The method of claim 1 wherein the step of determining thefirst quantitative indicator includes the step of determining a numberof occurrences of the term within the electronic document.
 3. The methodof claim 1 wherein the step of determining the first quantitativeindicator includes the step of determining a frequency of occurrence ofthe term within the electronic document.
 4. The method of claim 1wherein the step of determining the first quantitative indicatorincludes the step of determining a density of occurrence of the termwithin the electronic document.
 5. The method of claim 1 wherein thestep of determining the characteristic indicator includes the step ofdetermining the number of words comprising the term.
 6. The method ofclaim 1 wherein the step of determining the characteristic indicatorincludes the step of determining parts of speech included within theterm.
 7. The method of claim 1 wherein the step of determining thecharacteristic indicator includes the step of determining whether a wordwithin the term corresponds to a lexicon term.
 8. The method of claim 1including the step of summing second indicators, assigned to the termbased on occurrences of the term within a plurality of electronicdocuments, to generate a summed indicator.
 9. The method of claim 8including the step of assigning a confidence level indicator to the termbased on the summed indicator and the characteristic indicator.
 10. Themethod of claim 9 including the step of scaling the confidence levelindicator.
 11. The method of claim 1 including the step of determining athird indicator based on addressee information associated with theelectronic document.
 12. The method of claim 11 wherein the step ofdetermining the third indicator includes the identification of a levelof seniority within an organization of an addressee of the electronicdocument.
 13. The method of claim 11 wherein the step of determining thethird indicator includes determining an average level of senioritywithin an organization of all addressees of the electronic document. 14.The method of claim 11 wherein the step of determining the thirdindicator includes determining a level of seniority within anorganization of a most senior addressee of the electronic document. 15.The method of claim 12 including the step of generating a documentweight term utilizing the level of seniority of the addressee of theelectronic document.
 16. The method of claim 11 wherein the step ofdetermining the third indicator includes identification of a number ofaddressees of the electronic document.
 17. The method of claim 16including the step of generating a document weight term utilizing thenumber of addressees of the electronic document.
 18. The method of claim11 wherein the step of determining the third indicator includesidentification of an organizational group of at least one addressee ofthe electronic document.
 19. The method of claim 18 including the stepof generating a document weight term utilizing the organizational groupof the at least one addressee of the electronic document.
 20. Apparatusfor assigning a confidence level to a term within an electronicdocument, the apparatus comprising: a term extractor to extract the termfrom the electronic document; and confidence logic to determine a firstquantitative indicator based on a number of occurrences of the termwithin the electronic document; to determine a qualitative indicatorbased on a quality of the term; and assign a second indicator, derivedfrom the first quantitative indicator and the qualitative indicator, tothe term.
 21. The apparatus of claim 20 wherein the confidence logicdetermines a number of occurrences of the term within the electronicdocument.
 22. The apparatus of claim 20 wherein the confidence logicdetermines a frequency of occurrence of the term within the electronicdocument.
 23. The apparatus of claim 20 wherein the confidence logicdetermines a density of occurrence of the term within the electronicdocument.
 24. The apparatus of claim 20 wherein the confidence logicdetermines the number of words comprising the term.
 25. The apparatus ofclaim 20 wherein the confidence logic determines parts of speechincluded within the term.
 26. The apparatus of claim 20 wherein theconfidence logic determines whether a word within the term correspondsto a lexicon term.
 27. The apparatus of claim 20 wherein the confidencelogic sums second indicators, assigned to the term based on occurrencesof the term within a plurality of electronic documents, to generate asummed indicator.
 28. The apparatus of claim 27 wherein the confidencelogic assigns a confidence level indicator to the term based on thesummed quantitative indicator and the qualitative indicator.
 29. Theapparatus of claim 28 wherein the confidence logic scales the confidencelevel indicator.
 30. The apparatus of claim 20 wherein the confidencelogic determines a third indicator utilizing addressee informationassociated with the electronic document.
 31. The apparatus of claim 30wherein the confidence logic identifies a level of seniority within anorganization of an addressee of the electronic document.
 32. Theapparatus of claim 30 wherein the confidence logic determines an averagelevel of seniority within an organization of all addressees of theelectric document.
 33. The apparatus of claim 30 wherein the confidencelogic determines a level of seniority within an organization of a mostsenior addressee of the electronic document.
 34. The apparatus of claim30 wherein the confidence logic determines a level of seniority withinan organization of a most senior addressee of the electronic document.35. The apparatus of claim 30 wherein the confidence logic identifies anorganizational group of at least one addressee of the electronicdocument.
 36. Apparatus for assigning a confidence level to a termwithin an electronic document, the apparatus comprising: term extractionmeans for extracting a term from the electronic document; and confidencemeans for determining a first quantitative indicator based on the numberof occurrences of the term within the electronic document; fordetermining a characteristic indicator based on a characteristic of theterm; and for assigning a second indicator, derived from the firstquantitative indicator and the characteristic indicator, to the term.37. A machine-readable medium storing a sequence of instructions that,when executed by a machine, cause the machine to perform the steps of:determining a first quantitative indicator based on a number ofoccurrences of a term within an electronic document; determining aqualitative indicator based on a quality of the term; and assigning acomposite indicator to the term, the composite indicator being derivedfrom the first quantitative indicator and the qualitative indicator.